846 lines
37 KiB
Python
846 lines
37 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0
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"""Opt-in step caching for the diffusion transformer (First-Block-Cache).
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Across denoising steps a DiT's output changes little once the trajectory settles, so most of
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the transformer can be reused. First-Block-Cache (FBCache) computes the first block, and if
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its residual barely changed from the previous step (within ``threshold``) it skips the
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remaining blocks and reuses their cached output. diffusers ships it natively
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(``transformer.enable_cache(FirstBlockCacheConfig(...))`` for CacheMixin models, or the
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standalone ``apply_first_block_cache`` hook).
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Measured on Flux.1-dev (28 steps, 1024px, B200): ~1.4x on top of torch.compile (2.83 ->
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2.03 s) at LPIPS ~0.08 vs the no-cache output -- deep inside the speed-for-quality bar.
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OFF by default and a deliberate per-load opt-in, because the win scales with step count: a
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few-step distilled model (e.g. Z-Image-Turbo at ~8 steps) has almost no headroom and a
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single skipped step is a large fraction of the trajectory, so caching is for many-step
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models (Flux / Qwen-Image). It composes with torch.compile only with ``fullgraph=False``
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(the cache's compiler-disabled decision is a graph break), which the speed layer switches to
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automatically when a cache is engaged. Best-effort: an incompatible model (e.g. a transformer
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whose block signature the hook does not recognise) is caught and the load proceeds uncached.
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torch / diffusers imported lazily.
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"""
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from __future__ import annotations
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from typing import Any, Optional
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TC_OFF = "off"
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TC_AUTO = "auto"
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TC_FBCACHE = "fbcache"
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TC_MAGCACHE = "magcache"
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TC_MODES = (TC_FBCACHE, TC_MAGCACHE)
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# FBCache residual thresholds: higher skips more steps (faster, lower quality). The dense
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# bf16 default; a quantised transformer shifts the residual distribution, so it needs a
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# higher threshold for the cache to trigger at all (per ParaAttention's fp8 guidance).
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DEFAULT_FBCACHE_THRESHOLD = 0.08
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QUANT_FBCACHE_THRESHOLD = 0.12
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# MagCache (diffusers >= 0.39): skips whole steps from a PRE-CALIBRATED residual-magnitude
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# curve with an accumulated-error budget, a consecutive-skip cap, and a no-skip retention
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# window over the early steps -- so unlike FBCache the divergence from the uncached
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# trajectory is bounded. Measured on HunyuanVideo-1.5-720p (B200, 50 steps, 720p clip):
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# threshold 0.12 = 1.5x end-to-end at LPIPS 0.147 vs the same uncached stack with the SAME
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# composition (FBCache at its 0.08 default reached 2.4x but LPIPS 0.54: a brighter,
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# visibly different clip -- why the fbcache auto policy excludes this family).
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DEFAULT_MAGCACHE_THRESHOLD = 0.12
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MAGCACHE_MAX_SKIP_STEPS = 3
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MAGCACHE_RETENTION_RATIO = 0.2
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# ── cache quality presets ──────────────────────────────────────────────────────────
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# A user-facing speed/accuracy knob over the step cache's internals (threshold, skip cap,
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# retention window). "balanced" is exactly the pre-knob shipped behaviour; "quality"
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# trades most of the cache speedup for a near-lossless clip; "fast" skips more
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# aggressively. An explicit transformer_cache_threshold always overrides the preset's
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# threshold (the preset still supplies the magcache skip cap / retention window).
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CQ_QUALITY = "quality"
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CQ_BALANCED = "balanced"
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CQ_FAST = "fast"
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CACHE_QUALITY_LEVELS = (CQ_QUALITY, CQ_BALANCED, CQ_FAST)
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# MagCache preset -> (threshold, max_skip_steps, retention_ratio). Calibrated on
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# HunyuanVideo-1.5-720p (B200, 1280x720, 33 frames, 50 steps, pairwise LPIPS vs the same
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# uncached trim+cudnn+compile stack, WITH the compiled hook inners below): quality
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# (0.06, 2, 0.3) = 1.64x at LPIPS 0.050 (30 steps: 1.63x at 0.093) vs balanced
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# (0.12, 3, 0.2) = 2.17x at LPIPS 0.129 (30 steps: 2.02x at 0.201). Skip counts bind on
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# the cap + retention window below threshold ~0.12, which is why quality tightens all
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# three rather than just the threshold.
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_MAGCACHE_QUALITY_PRESETS: dict[str, tuple[float, int, float]] = {
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CQ_QUALITY: (0.06, 2, 0.3),
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CQ_BALANCED: (DEFAULT_MAGCACHE_THRESHOLD, MAGCACHE_MAX_SKIP_STEPS, MAGCACHE_RETENTION_RATIO),
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CQ_FAST: (0.24, MAGCACHE_MAX_SKIP_STEPS, MAGCACHE_RETENTION_RATIO),
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}
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# FBCache preset -> threshold (dense, quant-active). "balanced" keeps the measured
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# defaults (0.08 dense / 0.12 quantised); "quality" halves the trigger so the cache only
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# reuses when the first-block residual is nearly static; "fast" uses the quantised
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# threshold everywhere.
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_FBCACHE_QUALITY_THRESHOLDS: dict[str, tuple[float, float]] = {
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CQ_QUALITY: (0.04, 0.06),
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CQ_BALANCED: (DEFAULT_FBCACHE_THRESHOLD, QUANT_FBCACHE_THRESHOLD),
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CQ_FAST: (QUANT_FBCACHE_THRESHOLD, 0.15),
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}
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def normalize_cache_quality(value: Optional[str]) -> Optional[str]:
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"""Lower/strip a requested cache quality; None / "" / "auto" -> None (the loader
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resolves it per family via ``auto_cache_quality``). Raises ValueError for an
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unsupported value."""
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if value is None:
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return None
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normalized = str(value).strip().lower()
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if not normalized or normalized == "auto":
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return None
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if normalized not in CACHE_QUALITY_LEVELS:
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raise ValueError(
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f"Unsupported transformer_cache_quality '{value}'. Use one of: auto, "
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f"{', '.join(CACHE_QUALITY_LEVELS)}."
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)
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return normalized
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# Families whose UNSET cache quality resolves to the near-lossless "quality" preset
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# instead of "balanced". HunyuanVideo-1.5 (both repacks) measured with the compiled
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# cache inners (see _compile_hooked_block_inners): quality = 1.63-1.64x at pairwise
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# LPIPS 0.05 (50 steps) / 0.09 (30 steps) vs balanced's 2.02-2.17x at 0.13-0.20 --
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# the accuracy-first default keeps most of the speedup at under half the drift, and
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# balanced / fast stay one explicit request away. Families without a measured quality
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# point keep balanced (their pre-knob behaviour).
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_FAMILY_AUTO_CACHE_QUALITY: dict[str, str] = {
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"hunyuanvideo-1.5": CQ_QUALITY,
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"hunyuanvideo-1.5-720p": CQ_QUALITY,
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}
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def auto_cache_quality(family: Optional[str]) -> str:
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"""The cache quality preset an UNSET request resolves to for ``family``."""
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return _FAMILY_AUTO_CACHE_QUALITY.get(str(family or "").strip().lower(), CQ_BALANCED)
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# The auto policy's step-count bar: FBCache's win scales with step count (each skipped
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# step is a larger quality hit on a short trajectory), so auto engages it only at 20+
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# steps -- full "dev"-style schedules (28+) qualify, distilled turbo models (4-9) never do.
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FBCACHE_MIN_STEPS = 20
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# Per-family MagCache magnitude-ratio curves (MagCacheConfig.mag_ratios), calibrated with
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# diffusers' calibrate mode on the family base checkpoints at the default 50-step schedule
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# (720p clip, B200). The curve is checkpoint-dependent but highly stable where it matters:
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# the CFG cond/uncond branches differ by <= 0.014 and a 30-step calibration matches the
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# 50-step curve within 0.027 after nearest-interpolation, so ONE curve per family is
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# enough -- diffusers interpolates it to the actual step count. Conditional-branch curve
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# per the MagCache calibration guidance.
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_MAGCACHE_720P_RATIOS = (
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1.0,
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1.0226,
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1.0093,
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1.001,
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1.0008,
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1.0001,
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0.9995,
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1.0003,
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0.9998,
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0.9993,
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0.9994,
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0.9993,
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0.9997,
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1.0002,
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0.9994,
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0.9985,
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0.9987,
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0.9997,
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0.9979,
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0.9987,
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0.9985,
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0.9982,
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0.9977,
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0.998,
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0.9979,
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0.9971,
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0.9968,
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0.9967,
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0.9964,
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0.9965,
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0.9959,
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0.9954,
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0.995,
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0.9938,
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0.9942,
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0.9924,
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0.9924,
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0.9907,
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0.9905,
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0.9878,
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0.9867,
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0.9845,
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0.9808,
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0.9773,
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0.9715,
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0.9652,
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0.9529,
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0.9347,
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0.9011,
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0.83,
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)
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_MAGCACHE_480P_RATIOS = (
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1.0,
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1.0077,
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1.0138,
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1.0043,
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1.0029,
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0.9986,
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0.9966,
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1.0,
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1.0006,
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0.9996,
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0.9993,
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0.9986,
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1.0,
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0.9993,
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0.9966,
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0.9986,
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0.9988,
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0.9991,
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0.998,
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0.9977,
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0.9976,
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0.9971,
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0.9973,
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0.9969,
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0.996,
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0.9961,
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0.9949,
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0.9958,
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0.9933,
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0.9942,
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0.9941,
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0.9926,
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0.9929,
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0.9916,
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0.9923,
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0.9887,
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0.99,
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0.9882,
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0.9865,
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0.9833,
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0.9827,
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0.9791,
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0.9763,
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0.9718,
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0.9657,
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0.9563,
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0.9454,
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0.9264,
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0.8967,
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0.8382,
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)
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# Wan2.2-TI2V-5B, calibrated at 1280x704 / 33 frames / 50 steps on the family base
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# checkpoint (B200, trim-less compiled stack). Cond/uncond branches agree within
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# 0.0008, so one (conditional) curve serves both CFG contexts.
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_MAGCACHE_WAN5B_RATIOS = (
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1.0,
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0.9906,
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0.9996,
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0.9936,
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0.9968,
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0.9958,
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0.9956,
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0.9953,
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0.9957,
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0.9954,
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0.9941,
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0.9958,
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0.9933,
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0.9938,
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0.9948,
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0.9936,
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0.9948,
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0.9925,
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0.994,
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0.9927,
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0.9913,
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0.9919,
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0.9918,
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0.9907,
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0.989,
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0.9901,
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0.9892,
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0.9903,
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0.9884,
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0.9868,
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0.9851,
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0.9848,
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0.9849,
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0.9831,
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0.9818,
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0.9804,
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0.9781,
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0.9756,
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0.9733,
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0.9717,
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0.9688,
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0.9646,
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0.9611,
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0.9559,
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0.9503,
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0.9443,
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0.938,
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0.9315,
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0.9227,
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0.9208,
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)
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# All curves are calibrated at the family's default 50-step schedule. A single-DiT
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# curve therefore has 50 entries and MagCacheConfig interpolates it to the actual step
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# count. A dual-expert MoE (Wan2.2-A14B) runs each expert on a SLICE of the schedule
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# (the boundary_ratio split) and the MagCache hook counts each expert's OWN forwards
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# from 0, so each expert carries its own curve, keyed "family::transformer_2" for the
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# second expert, whose length is the number of steps that expert ran during the 50-step
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# calibration; engage-time scales it proportionally to the requested step count (the
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# boundary split is a fixed fraction of the schedule for a given checkpoint).
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_MAGCACHE_CALIBRATION_STEPS = 50
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_MAGCACHE_FAMILY_RATIOS: dict[str, tuple[float, ...]] = {
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"hunyuanvideo-1.5": _MAGCACHE_480P_RATIOS,
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"hunyuanvideo-1.5-720p": _MAGCACHE_720P_RATIOS,
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"wan2.2-ti2v-5b": _MAGCACHE_WAN5B_RATIOS,
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}
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def _magcache_ratio_key(family: Optional[str], expert: Optional[str]) -> str:
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"""The `_MAGCACHE_FAMILY_RATIOS` key for a (family, expert) pair: the bare family
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name for the primary ``transformer``, ``family::expert`` for a second expert."""
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fam = str(family or "").strip().lower()
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exp = str(expert or "").strip().lower()
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if exp in ("", "transformer"):
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return fam
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return f"{fam}::{exp}"
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# Families whose AUTO step-cache decision engages MagCache instead of FBCache. On
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# HunyuanVideo-1.5 FBCache free-runs (no skip cap, no error budget) and derails the
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# trajectory (LPIPS 0.54 + a luma shift at its default threshold), while MagCache holds
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# the same composition at 1.5x -- see the constants above. On Wan2.2-TI2V-5B both modes
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# stay composition-true, but MagCache dominates the accuracy/speed frontier (B200,
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# 1280x704/33f/50 steps, pairwise LPIPS vs the same uncached compiled stack): balanced
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# MagCache 1.65x at 0.034 vs FBCache 0.08 at 1.49x/0.031, and at the fast points 1.73x
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# at 0.044 vs 1.71x at 0.083 -- FBCache's error grows unboundedly past its threshold
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# while MagCache's budget caps it. On Wan2.2-A14B (dual-expert MoE) the OPPOSITE holds
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# (B200, 1280x720/33f/50 steps, per-expert calibrated curves, same pairwise protocol):
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# FBCache 0.12 at 2.88x/0.128 dominates balanced MagCache (1.80x/0.145) and FBCache
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# 0.08 sits at 1.28x/0.098 vs MagCache quality's 1.14x/0.074 -- the 16-step high-noise
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# expert leaves MagCache too few forwards to skip within its error budget -- so the
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# family keeps the FBCache default and no calibrated curve ships (an explicit magcache
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# request runs uncached with a warning rather than engaging a measured-worse mode).
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# Every other family keeps the measured FBCache default. An EXPLICIT
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# "fbcache"/"magcache" request always wins.
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_FAMILY_AUTO_CACHE_MODE: dict[str, str] = {
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"hunyuanvideo-1.5": TC_MAGCACHE,
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"hunyuanvideo-1.5-720p": TC_MAGCACHE,
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"wan2.2-ti2v-5b": TC_MAGCACHE,
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}
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def auto_cache_mode(family: Optional[str]) -> str:
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"""The cache mode the AUTO policy engages for ``family`` (mode only; the step-count
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bar and the engage call are the caller's job). MagCache additionally needs a
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calibrated ratio curve: a family routed here without one runs uncached (the
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apply_step_cache magcache branch checks), never silently falls back to FBCache."""
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return _FAMILY_AUTO_CACHE_MODE.get(str(family or "").strip().lower(), TC_FBCACHE)
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def normalize_transformer_cache(value: Optional[str]) -> Optional[str]:
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"""Lower/strip a requested cache mode; None / "" / "none" / "off" -> None (disabled),
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"auto" -> TC_AUTO (the loader decides from the step count).
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Raises ValueError for an unsupported value so a bad request is rejected cheaply."""
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if value is None:
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return None
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normalized = str(value).strip().lower().replace("-", "_")
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if not normalized or normalized in ("none", "off"):
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return None
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if normalized == TC_AUTO:
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return TC_AUTO
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if normalized not in TC_MODES:
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raise ValueError(
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f"Unsupported transformer_cache '{value}'. Use one of: off, auto, "
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f"{', '.join(TC_MODES)}."
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)
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return normalized
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# Transformer block classes whose FBCache metadata is missing from the installed
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# diffusers. The First-Block-Cache hook reads each block's (hidden_states,
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# encoder_hidden_states) return layout from TransformerBlockRegistry; diffusers 0.39
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# registers the HunyuanVideo 1.0 blocks but not the 1.5 ones, so enable_cache raises
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# "Model class HunyuanVideo15TransformerBlock not registered" on a DiT that is
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# otherwise fully cache-compatible: CacheMixin, one homogeneous ``transformer_blocks``
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# list of residual-additive dual-stream blocks returning (hidden_states,
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# encoder_hidden_states) -- the exact layout of the registered 1.0 block. Keyed by the
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# TRANSFORMER class name so only a family that needs the patch pays for it, and probed
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# via TransformerBlockRegistry.get first so a diffusers release that ships the
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# registration natively makes this a no-op.
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# transformer class -> ((block module, block class, hs index, ehs index), ...)
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#
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# LTX-2 is DELIBERATELY absent: its LTX2VideoTransformerBlock is also unregistered in
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# diffusers 0.39, but it returns (hidden_states, audio_hidden_states) -- a JOINT
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# video+audio stream -- while both cache hook families cache/skip only the single
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# ``hidden_states`` stream and, on a skipped step, fetch the parameter literally named
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# ``encoder_hidden_states`` (the TEXT embeddings) for the second return slot. A naive
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# registration would therefore feed text embeddings into the next block's audio input
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# on every skipped step. Step caching for LTX-2 needs a dual-stream cache
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# implementation, not a metadata entry; until then the family runs uncached (verified:
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# enable_cache raises "not registered" and the load proceeds uncached, and the
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# distilled LTX-2.3 checkpoints run 8-step schedules below FBCACHE_MIN_STEPS anyway).
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_EXTRA_BLOCK_METADATA: dict[str, tuple[tuple[str, str, int, Optional[int]], ...]] = {
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"HunyuanVideo15Transformer3DModel": (
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(
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"diffusers.models.transformers.transformer_hunyuan_video15",
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"HunyuanVideo15TransformerBlock",
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0,
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1,
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),
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),
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}
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def _ensure_block_metadata_registered(transformer: Any, logger: Any = None) -> None:
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"""Register the missing FBCache block metadata for ``transformer``'s family (see
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``_EXTRA_BLOCK_METADATA``). Best-effort: a failure just leaves enable_cache to raise
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its own error and the load runs uncached, exactly as before this patch."""
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specs = _EXTRA_BLOCK_METADATA.get(type(transformer).__name__)
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if not specs:
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return
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try:
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import importlib
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from diffusers.hooks._helpers import TransformerBlockMetadata, TransformerBlockRegistry
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for module_name, cls_name, hs_index, ehs_index in specs:
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block_cls = getattr(importlib.import_module(module_name), cls_name)
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try:
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TransformerBlockRegistry.get(block_cls)
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continue # a newer diffusers registers it natively
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except ValueError:
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pass
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TransformerBlockRegistry.register(
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|
block_cls,
|
|
TransformerBlockMetadata(
|
|
return_hidden_states_index = hs_index,
|
|
return_encoder_hidden_states_index = ehs_index,
|
|
),
|
|
)
|
|
if logger is not None:
|
|
logger.info("diffusion.cache: registered %s block metadata for fbcache", cls_name)
|
|
except Exception as exc: # noqa: BLE001 -- best-effort; enable_cache surfaces the real error
|
|
_warn(logger, "block metadata registration", exc)
|
|
|
|
|
|
def _invalidate_child_registry_cache(transformer: Any) -> None:
|
|
"""Drop the HookRegistry's cached child-registry list after (un)installing hooks.
|
|
|
|
``cache_context`` propagates the state context through ``_get_child_registries``,
|
|
which diffusers 0.39 caches on first use. An UNCACHED generation already calls
|
|
``cache_context`` (the pipeline wraps every denoise call), creating the
|
|
transformer-level registry with an EMPTY cached child list -- so a later
|
|
``enable_cache`` (the auto step-count toggle engaging FBCache mid-session) installs
|
|
block hooks that ``_set_context`` never reaches, and the first cached forward dies
|
|
with "No context is set". Invalidate the stale cache so the next ``cache_context``
|
|
rebuilds it over the freshly hooked blocks. Best-effort and cheap (one attribute)."""
|
|
registry = getattr(transformer, "_diffusers_hook", None)
|
|
if registry is not None and getattr(registry, "_child_registries_cache", None) is not None:
|
|
try:
|
|
registry._child_registries_cache = None
|
|
except Exception: # noqa: BLE001 -- diffusers internals moved; leave as-is
|
|
pass
|
|
|
|
|
|
# diffusers' cache hook registry names whose compute branch we re-point at a compiled
|
|
# inner forward (leader = the measuring first block, block = the remaining ones); both
|
|
# hook families share the fn_ref layout.
|
|
_CACHE_HOOK_NAMES = (
|
|
"mag_cache_leader_block_hook",
|
|
"mag_cache_block_hook",
|
|
"fbc_leader_block_hook",
|
|
"fbc_block_hook",
|
|
)
|
|
|
|
|
|
def _compile_hooked_block_inners(transformer: Any, logger: Any = None) -> int:
|
|
"""Restore the regional compile on cache-hooked blocks' COMPUTED steps.
|
|
|
|
``enable_cache`` replaces each block's ``forward`` with the hook's ``new_forward``
|
|
(stashing the pre-hook bound method in ``fn_ref.original_forward``), whose skip
|
|
decision is data-dependent Python: MagCache ``@torch.compiler.disable``s the whole
|
|
``new_forward`` (recursive -- the compute branch runs EAGER), and even FBCache's
|
|
traceable ``new_forward`` graph-breaks around its disabled threshold decision,
|
|
which on some archs (measured: Qwen-Image) drops the compute branch's call into
|
|
``original_forward`` out of the compiled region -- the block's regional compile
|
|
artifact (``_compiled_call_impl``) is never reached and the cache forfeits the
|
|
compile win on every non-skipped step. An explicitly ``torch.compile``d callable
|
|
re-enables
|
|
dynamo for its own extent even inside a disabled frame, so re-pointing
|
|
``fn_ref.original_forward`` at a compiled wrapper of the same bound method restores
|
|
compiled compute steps while the skip decision stays eager exactly as designed.
|
|
Measured (B200, scripts/image_speedmem_bench.py): Qwen-Image FBCache computed steps
|
|
91.8 -> 71.2 ms (= the uncached compiled rate), 1.21x end to end; FLUX.1-dev is
|
|
neutral (its FBCache ``new_forward`` happens to trace, so computed steps were
|
|
already compiled -- same-process armed vs unarmed latents bit-identical); on the
|
|
video DiT balanced MagCache went 39.4 -> 26.9 s at 50 steps.
|
|
|
|
Only blocks the speed layer actually compiled are armed (``_compiled_call_impl``
|
|
guard -- eager tiers stay untouched), and only when ``original_forward`` is a plain
|
|
bound method (a stacked hook chain, e.g. offload, captures a partial and is
|
|
skipped). Idempotent via the ``_unsloth_orig_inner`` marker; best-effort. Returns
|
|
the number of hooks armed."""
|
|
try:
|
|
import torch
|
|
except Exception: # noqa: BLE001 -- no torch, nothing to arm
|
|
return 0
|
|
armed = 0
|
|
try:
|
|
for module in transformer.modules():
|
|
registry = getattr(module, "_diffusers_hook", None)
|
|
if registry is None or getattr(module, "_compiled_call_impl", None) is None:
|
|
continue
|
|
hooks = getattr(registry, "hooks", None) or {}
|
|
for name in _CACHE_HOOK_NAMES:
|
|
hook = hooks.get(name)
|
|
fn_ref = getattr(hook, "fn_ref", None) if hook is not None else None
|
|
orig = getattr(fn_ref, "original_forward", None)
|
|
if orig is None or getattr(hook, "_unsloth_orig_inner", None) is not None:
|
|
continue
|
|
if getattr(orig, "__self__", None) is None:
|
|
continue # not the plain bound method; arming would miss the block
|
|
# fullgraph=False / dynamic=True: a cache is active by definition (its
|
|
# decision points graph-break) and this matches the default tier the
|
|
# regional compile used. Dynamo caches per code object, so re-arming
|
|
# after a toggle is effectively free (~0.03 s).
|
|
fn_ref.original_forward = torch.compile(orig, fullgraph = False, dynamic = True)
|
|
hook._unsloth_orig_inner = orig
|
|
armed += 1
|
|
except Exception as exc: # noqa: BLE001 -- best-effort: the cache still works eager
|
|
_warn(logger, "cache-hook inner compile", exc)
|
|
return armed
|
|
if armed and logger is not None:
|
|
logger.info(
|
|
"diffusion.cache: %d cache-hooked block(s) armed with compiled inner forwards",
|
|
armed,
|
|
)
|
|
return armed
|
|
|
|
|
|
def _restore_hooked_block_inners(transformer: Any) -> None:
|
|
"""Undo ``_compile_hooked_block_inners``: put the plain bound methods back and clear
|
|
the markers. MUST run before ``disable_cache`` -- ``remove_hook`` splices
|
|
``fn_ref.original_forward`` back into ``module.forward``, and leaving the compiled
|
|
wrapper there would pin a stale compiled callable onto the uncached path."""
|
|
try:
|
|
modules = list(transformer.modules())
|
|
except Exception: # noqa: BLE001 -- not a torch module (tests/fakes): nothing armed
|
|
return
|
|
for module in modules:
|
|
registry = getattr(module, "_diffusers_hook", None)
|
|
if registry is None:
|
|
continue
|
|
hooks = getattr(registry, "hooks", None) or {}
|
|
for name in _CACHE_HOOK_NAMES:
|
|
hook = hooks.get(name)
|
|
orig = getattr(hook, "_unsloth_orig_inner", None) if hook is not None else None
|
|
if orig is None:
|
|
continue
|
|
try:
|
|
hook.fn_ref.original_forward = orig
|
|
hook._unsloth_orig_inner = None
|
|
except Exception: # noqa: BLE001 -- per-hook best-effort
|
|
pass
|
|
|
|
|
|
def _pipeline_opens_cache_context(pipe: Any) -> bool:
|
|
"""Whether the pipeline enters ``transformer.cache_context(...)`` in its denoise loop.
|
|
The First-Block-Cache hook requires it at run time, and a CacheMixin transformer alone
|
|
does NOT guarantee it: Flux Kontext / img2img / inpaint / controlnet reuse the CacheMixin
|
|
FluxTransformer2DModel but never open a cache_context. Read from the pipeline ``__call__``
|
|
source, resolved off the instance so a per-expert proxy view (``_SecondDiTView``)
|
|
delegates to the real pipe; if it cannot be read, report False so the cache stays off."""
|
|
import inspect
|
|
|
|
call = getattr(pipe, "__call__", None)
|
|
if call is None:
|
|
return False
|
|
try:
|
|
src = inspect.getsource(call)
|
|
except (OSError, TypeError):
|
|
return False
|
|
# Match the actual call `cache_context(` -- a bare mention in a comment/docstring lacks
|
|
# the paren, so this does not false-positive on prose.
|
|
return "cache_context(" in src
|
|
|
|
|
|
def apply_step_cache(
|
|
pipe: Any,
|
|
*,
|
|
mode: Optional[str],
|
|
threshold: Optional[float] = None,
|
|
quant_active: bool = False,
|
|
family: Optional[str] = None,
|
|
steps: Optional[int] = None,
|
|
quality: Optional[str] = None,
|
|
expert: Optional[str] = None,
|
|
logger: Any = None,
|
|
) -> Optional[str]:
|
|
"""Engage step caching on ``pipe.transformer``. Returns the mode actually engaged, or
|
|
None when disabled / unsupported (the load then runs uncached). ``threshold`` overrides
|
|
the default; ``quant_active`` raises the FBCache default so the cache still triggers on
|
|
a quantised transformer. ``quality`` picks the preset parameter set (threshold + the
|
|
magcache skip cap / retention window); an explicit ``threshold`` still wins over the
|
|
preset's threshold. The magcache mode additionally needs ``family`` (to look up the
|
|
calibrated ratio curve) and ``steps`` (MagCache interpolates that curve over the
|
|
configured step count and sizes its no-skip retention window from it); a dual-expert
|
|
MoE caller passes ``expert`` (the pipe attribute the view exposes, e.g.
|
|
"transformer_2") so each expert gets ITS OWN calibrated curve -- the experts split
|
|
the schedule at the boundary timestep, and the hook counts each expert's own
|
|
forwards from 0, so one shared full-schedule curve would be misaligned for both.
|
|
Best-effort: never raises for an incompatible model."""
|
|
mode = normalize_transformer_cache(mode)
|
|
if mode is None or mode == TC_AUTO:
|
|
# AUTO must be resolved by the loader (step-count policy) before reaching the
|
|
# engage call; treat a stray auto as off rather than crashing the load.
|
|
return None
|
|
transformer = getattr(pipe, "transformer", None)
|
|
if transformer is None:
|
|
return None
|
|
quality = normalize_cache_quality(quality) or CQ_BALANCED
|
|
if mode == TC_MAGCACHE:
|
|
preset_thr, mag_skip, mag_retention = _MAGCACHE_QUALITY_PRESETS[quality]
|
|
thr = threshold if threshold is not None else preset_thr
|
|
else:
|
|
dense_thr, quant_thr = _FBCACHE_QUALITY_THRESHOLDS[quality]
|
|
thr = threshold if threshold is not None else (quant_thr if quant_active else dense_thr)
|
|
# Engage only via the transformer's native enable_cache (the diffusers CacheMixin path):
|
|
# the lower-level apply_first_block_cache hook would install on a non-CacheMixin
|
|
# transformer too (e.g. Z-Image), whose pipeline opens no cache_context and would crash
|
|
# the first generation -- so a model without enable_cache runs uncached per the
|
|
# best-effort contract instead of being reported as cached and then failing.
|
|
enable_cache = getattr(transformer, "enable_cache", None)
|
|
if not callable(enable_cache):
|
|
_warn(logger, mode, RuntimeError("transformer has no cache_context (not a CacheMixin)"))
|
|
return None
|
|
# A CacheMixin transformer is necessary but NOT sufficient: the First-Block-Cache hook
|
|
# raises "No context is set" on the first forward unless the PIPELINE wraps its denoise
|
|
# loop in transformer.cache_context(...). Flux Kontext / img2img / inpaint / controlnet
|
|
# reuse the CacheMixin FluxTransformer2DModel yet their __call__ opens no cache_context,
|
|
# so engaging FBCache there would crash every default generation -- run uncached instead.
|
|
if not _pipeline_opens_cache_context(pipe):
|
|
_warn(
|
|
logger, mode, RuntimeError("pipeline __call__ opens no cache_context; running uncached")
|
|
)
|
|
return None
|
|
# Some cache-compatible block classes are missing from the installed diffusers'
|
|
# FBCache metadata registry (HunyuanVideo-1.5); register them before enable_cache.
|
|
# Both hook families (FBCache / MagCache) read the same block metadata.
|
|
_ensure_block_metadata_registered(transformer, logger)
|
|
try:
|
|
if mode == TC_MAGCACHE:
|
|
ratio_key = _magcache_ratio_key(family, expert)
|
|
ratios = _MAGCACHE_FAMILY_RATIOS.get(ratio_key)
|
|
if ratios is None:
|
|
# No silent FBCache fallback: the family was routed to magcache exactly
|
|
# because FBCache derails it, so an uncalibrated checkpoint runs uncached.
|
|
_warn(
|
|
logger,
|
|
mode,
|
|
RuntimeError(f"no calibrated mag_ratios for '{ratio_key}'"),
|
|
)
|
|
return None
|
|
if not steps or int(steps) <= 0:
|
|
_warn(logger, mode, RuntimeError("magcache needs the step count to engage"))
|
|
return None
|
|
from diffusers.hooks import MagCacheConfig
|
|
|
|
# A full-schedule curve (one entry per calibration step) interpolates to the
|
|
# requested step count directly. An expert SUB-curve (dual-expert MoE) covers
|
|
# only that expert's slice of the calibration schedule, and the hook indexes
|
|
# it by the expert's own forward count, so scale its configured step count by
|
|
# the same steps/calibration ratio: the boundary split is a fixed fraction of
|
|
# the schedule, so the expert runs ~len(ratios) * steps / 50 forwards.
|
|
num_steps = int(steps)
|
|
if len(ratios) != _MAGCACHE_CALIBRATION_STEPS:
|
|
num_steps = max(
|
|
1, round(len(ratios) * int(steps) / _MAGCACHE_CALIBRATION_STEPS)
|
|
)
|
|
config: Any = MagCacheConfig(
|
|
threshold = thr,
|
|
max_skip_steps = mag_skip,
|
|
retention_ratio = mag_retention,
|
|
num_inference_steps = num_steps,
|
|
mag_ratios = list(ratios),
|
|
)
|
|
# The curve is interpolated over the CONFIGURED step count, so the marker
|
|
# carries it: the auto toggle re-engages on a step-count change.
|
|
marker = f"{mode}@{thr}#s{int(steps)}"
|
|
else:
|
|
try:
|
|
from diffusers import FirstBlockCacheConfig
|
|
except ImportError: # older diffusers exports it only from diffusers.hooks
|
|
from diffusers.hooks import FirstBlockCacheConfig
|
|
|
|
config = FirstBlockCacheConfig(threshold = thr)
|
|
marker = f"{mode}@{thr}"
|
|
enable_cache(config)
|
|
# A prior uncached generation may have frozen an empty child-registry list on
|
|
# the transformer's HookRegistry; the block hooks just installed would then
|
|
# never receive the cache context. Must follow every enable_cache.
|
|
_invalidate_child_registry_cache(transformer)
|
|
# If the blocks are already regionally compiled (the generation-time toggle
|
|
# path: compile ran at load), re-point the fresh hooks' compute branch at
|
|
# compiled inners; the load path (cache before compile) is armed by
|
|
# _compile_repeated_blocks instead. No-op when nothing is compiled.
|
|
_compile_hooked_block_inners(transformer, logger)
|
|
try:
|
|
transformer._unsloth_step_cache = marker
|
|
except Exception: # noqa: BLE001 — marker is best-effort
|
|
pass
|
|
if logger is not None:
|
|
logger.info("diffusion.cache: %s engaged (threshold=%s)", mode, thr)
|
|
return mode
|
|
except Exception as exc: # noqa: BLE001 — incompatible model -> run uncached
|
|
# enable_cache can fail after hooking some blocks; drop any partial hooks so
|
|
# the reported-uncached model doesn't actually run half-cached. Any armed
|
|
# compiled inners must be restored FIRST (remove_hook splices original_forward
|
|
# back into module.forward).
|
|
_restore_hooked_block_inners(transformer)
|
|
try:
|
|
transformer.disable_cache()
|
|
except Exception: # noqa: BLE001
|
|
pass
|
|
_warn(logger, mode, exc)
|
|
return None
|
|
|
|
|
|
def effective_denoise_steps(steps: int, strength: Optional[float]) -> int:
|
|
"""The number of steps diffusers ACTUALLY denoises for a request.
|
|
|
|
An image-conditioned workflow with ``strength`` < 1 (img2img / upscale / inpaint) runs
|
|
only a fraction of ``num_inference_steps``: diffusers' ``get_timesteps`` computes
|
|
``init_timestep = min(int(num_inference_steps * strength), num_inference_steps)`` and
|
|
denoises exactly ``init_timestep`` steps -- the product is FLOORED, not rounded. The auto
|
|
step-cache policy must key on THIS count -- e.g. a 28-step upscale at strength 0.35 runs
|
|
``int(9.8) = 9`` real steps, exactly the short trajectory FBCache should stay off (each
|
|
skipped step is a large quality hit). ``strength`` None (txt2img / reference) or >= 1 -> the
|
|
full count.
|
|
"""
|
|
s = int(steps)
|
|
if strength is None or float(strength) >= 1.0:
|
|
return s
|
|
return max(1, min(int(s * float(strength)), s))
|
|
|
|
|
|
def effective_request_strength(
|
|
request_strength: Optional[float],
|
|
has_init_image: bool,
|
|
pipe_accepts_strength: bool,
|
|
pipe_default_strength: Any,
|
|
) -> Optional[float]:
|
|
"""The strength the pipe will ACTUALLY apply, for keying the auto step-cache policy.
|
|
|
|
Only image-conditioned pipelines that take ``strength`` apply it (txt2img / a pipe without
|
|
the kwarg run the full trajectory -> None). When the request omits ``strength`` the loader
|
|
does NOT pass the kwarg, so the pipe runs its OWN signature default (< 1 for every img2img /
|
|
inpaint pipeline here, e.g. 0.6); the policy must key on that default, not the full step
|
|
count, or FBCache engages on a fraction of the advertised steps. A non-numeric default
|
|
(``inspect.Parameter.empty``) falls back to the full count (None).
|
|
"""
|
|
if not (has_init_image and pipe_accepts_strength):
|
|
return None
|
|
if request_strength is not None:
|
|
return request_strength
|
|
return pipe_default_strength if isinstance(pipe_default_strength, (int, float)) else None
|
|
|
|
|
|
def _disengage_step_cache(
|
|
transformer: Any,
|
|
*,
|
|
reason: str,
|
|
logger: Any = None,
|
|
) -> bool:
|
|
"""disable_cache + clear the marker; True when the transformer is now uncached."""
|
|
disable_cache = getattr(transformer, "disable_cache", None)
|
|
if not callable(disable_cache):
|
|
return False
|
|
try:
|
|
# Before remove_hook splices fn_ref.original_forward back into module.forward:
|
|
# the compiled inner wrappers must not leak onto the uncached path.
|
|
_restore_hooked_block_inners(transformer)
|
|
disable_cache()
|
|
transformer._unsloth_step_cache = None
|
|
if logger is not None:
|
|
logger.info("diffusion.cache: step cache disengaged (%s)", reason)
|
|
return True
|
|
except Exception as exc: # noqa: BLE001 -- keep the cache rather than crash
|
|
_warn(logger, "step cache disable", exc)
|
|
return False
|
|
|
|
|
|
def maybe_toggle_step_cache(
|
|
pipe: Any,
|
|
*,
|
|
steps: int,
|
|
quant_active: bool = False,
|
|
threshold: Optional[float] = None,
|
|
mode: str = TC_FBCACHE,
|
|
family: Optional[str] = None,
|
|
quality: Optional[str] = None,
|
|
expert: Optional[str] = None,
|
|
logger: Any = None,
|
|
) -> Optional[str]:
|
|
"""Generation-time enable/disable for an AUTO cache decision, keyed on the actual
|
|
step count: engage ``mode`` (the family's auto cache mode) at ``FBCACHE_MIN_STEPS``
|
|
or more, run uncached below it. Idempotent (the ``_unsloth_step_cache`` marker tracks
|
|
the engaged state), so calling it on every generation is cheap -- except a magcache
|
|
step-count change, which re-engages so the ratio curve is re-interpolated over the
|
|
actual schedule. Only the loader's auto path calls this; an explicit user choice is
|
|
never toggled. Returns the mode now active (or None when uncached)."""
|
|
transformer = getattr(pipe, "transformer", None)
|
|
if transformer is None:
|
|
return None
|
|
engaged = getattr(transformer, "_unsloth_step_cache", None)
|
|
want = int(steps) >= FBCACHE_MIN_STEPS
|
|
if (
|
|
want
|
|
and engaged
|
|
and mode == TC_MAGCACHE
|
|
# endswith, not substring: "#s5" would match inside "#s50".
|
|
and not str(engaged).endswith(f"#s{int(steps)}")
|
|
and _disengage_step_cache(
|
|
transformer, reason = f"magcache re-interpolating for {steps} steps", logger = logger
|
|
)
|
|
):
|
|
engaged = None
|
|
if want and not engaged:
|
|
return apply_step_cache(
|
|
pipe,
|
|
mode = mode,
|
|
threshold = threshold,
|
|
quant_active = quant_active,
|
|
family = family,
|
|
steps = steps,
|
|
quality = quality,
|
|
expert = expert,
|
|
logger = logger,
|
|
)
|
|
if not want and engaged:
|
|
if _disengage_step_cache(
|
|
transformer,
|
|
reason = f"auto: {steps} steps < {FBCACHE_MIN_STEPS}",
|
|
logger = logger,
|
|
):
|
|
return None
|
|
return mode
|
|
return mode if engaged else None
|
|
|
|
|
|
def _warn(logger: Any, what: str, exc: Exception) -> None:
|
|
if logger is not None:
|
|
logger.warning("diffusion.cache: %s unavailable (%s); running uncached", what, exc)
|